Center for Advanced Study of Language

Permanent URI for this communityhttp://hdl.handle.net/1903/11609

Browse

Author: search.filters.author.Doermann, DavidSubject: search.filters.subject.XML

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
    (Trojina Institute for Applied Slovene Studies, 2011-11) Zajic, David; Maxwell, Michael; Doermann, David; Rodrigues, Paul; Bloodgood, Michael
    We describe a paradigm for combining manual and automatic error correction of noisy structured lexicographic data. Modifications to the structure and underlying text of the lexicographic data are expressed in a simple, interpreted programming language. Dictionary Manipulation Language (DML) commands identify nodes by unique identifiers, and manipulations are performed using simple commands such as create, move, set text, etc. Corrected lexicons are produced by applying sequences of DML commands to the source version of the lexicon. DML commands can be written manually to repair one-off errors or generated automatically to correct recurring problems. We discuss advantages of the paradigm for the task of editing digital bilingual dictionaries.
  • Thumbnail Image
    Item
    Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
    (Trojina Institute for Applied Slovene Studies, 2011-11) Rodrigues, Paul; Zajic, David; Doermann, David; Bloodgood, Michael; Ye, Peng
    Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent lexical entries, and utilize descendants of these repeating elements to represent the structure within each lexical entry, in the form of an XML tree. In many cases, dictionaries are published that have errors and inconsistencies that are expensive to find manually. This paper discusses a method for dictionary writers to quickly audit structural regularity across entries in a dictionary by using statistical language modeling. The approach learns the patterns of XML nodes that could occur within an XML tree, and then calculates the probability of each XML tree in the dictionary against these patterns to look for entries that diverge from the norm.